59 citations · 443 across the 46 of their papers we have counts for
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Model identification and local linear convergence of coordinate descent
Quentin Klopfenstein, Quentin Bertrand, Alexandre Gramfort +2
For composite nonsmooth optimization problems, Forward-Backward algorithm achieves model identification (e.g. support identification for the Lasso) after a finite number of iterati…
Screening Rules and its Complexity for Active Set Identification
Eugene Ndiaye, Olivier Fercoq, Joseph Salmon
Screening rules were recently introduced as a technique for explicitly identifying active structures such as sparsity, in optimization problem arising in machine learning. This has…
Statistical control for spatio-temporal MEG/EEG source imaging with desparsified multi-task Lasso
Jérôme-Alexis Chevalier, Alexandre Gramfort, Joseph Salmon +1
Detecting where and when brain regions activate in a cognitive task or in a given clinical condition is the promise of non-invasive techniques like magnetoencephalography (MEG) or…
Provably Convergent Working Set Algorithm for Non-Convex Regularized Regression
Alain Rakotomamonjy, Rémi Flamary, Gilles Gasso +1
Owing to their statistical properties, non-convex sparse regularizers have attracted much interest for estimating a sparse linear model from high dimensional data. Given that the s…
Implicit differentiation of Lasso-type models for hyperparameter optimization
Quentin Bertrand, Quentin Klopfenstein, Mathieu Blondel +3
Setting regularization parameters for Lasso-type estimators is notoriously difficult, though crucial in practice. The most popular hyperparameter optimization approach is grid-sear…
Support recovery and sup-norm convergence rates for sparse pivotal estimation
Mathurin Massias, Quentin Bertrand, Alexandre Gramfort +1
In high dimensional sparse regression, pivotal estimators are estimators for which the optimal regularization parameter is independent of the noise level. The canonical pivotal est…